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Non-Markovian effect enhanced quantum noises in a coherent Ising machine

Optics Express 2025年第11期33卷 22208-22221页

作者： Chen-Rui Fan Bo Lu Yi-Xuan Yao Qing Ai Chuan Wang School of Artificial Intelligence Beijing Normal University Beijing 100875 China Laboratory for Advanced Computing and Intelligence Engineering Zhengzhou 450001 China lubo@*** School of Physics and Astronomy Applied Optics Beijing Area Major Laboratory Beijing 100875 China Key Laboratory of Multiscale Spin Physics Ministry of Education Beijing Normal University Beijing 100875 China wangchuan@***

Combinatorial optimization problems (COPs) constitute a fundamental class of computational challenges with extensive applications across scientific and industrial domains. The emergence of the coherent Ising machine (CIM) as a computational paradigm has demonstrated exceptional capabilities in rapidly generating arbitrary spin configurations and effectively solving large-scale COPs. Despite its promising potential, the CIM framework encounters an inherent limitation related to amplitude heterogeneity. Current approaches to this limitation primarily rely on Gaussian approximations to model quantum noise in the system’s amplitude dynamics within the original CIM framework. However, these methods generally fail to account for the temporal and periodic non-Markovian characteristics of pulses in practical environments. In this study, we introduce temporally correlated non-Markovian noise into the dynamics of both conventional and spiking neural network-based CIM systems, investigating their performance through comprehensive hyperparameter analysis. Our method incorporates a detailed examination of hyperparameter selection and its influence on algorithmic efficiency. Through extensive numerical simulations, we demonstrate that the integration of non-Markovian noise significantly accelerates variance evolution in both canonical coordinates and amplitude dynamics over time. The proposed approach not only exhibits enhanced computational performance but also shows superior scalability for large-scale problem instances. These findings suggest that our approach offers distinct advantages in addressing complex COPs, potentially opening new avenues for efficient optimization in various applications.

关键词： Neural networks Nonlinear optical fibers Numerical simulation Optical parametric oscillators Optical systems Quantum noise

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NetPrompt: Neural Network Prompting Enhances Event Extraction in Large Language Models

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IEEE Transactions on Big Data 2025年

作者： Mu, Lin Cheng, Yide Shen, Jun Zhang, Yiwen Zhong, Hong School of Computer Science and Technology Anhui University Anhui Hefei230601 China The Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education the Anhui Engineering Laboratory of IoT Security Technologies the School of Computer Science and Technology the Institute of Physical Science and Information Technology Anhui University Hefei230039 China

Event Extraction involves extracting event-related information such as event types and event arguments from context, which has long been tackled through well-designed neural networks or fine-tuned pre-trained language models. These approaches require substantial annotated data for tuning parameters and are resource-intensive. Recently, Prompting strategies with frozen parameters, such as Chain-of-Thought and Self-Consistency, have delivered success in NLP using LLMs by generating intermediate thought steps. However, they suffer from the challenge of error propagation and lack of interaction between different thoughts. In this paper, we propose Neural Network-based Prompting (NetPrompt), a novel network-structured prompting strategy for event extraction. The core idea behind NetPrompt is to imitate the excellent information integration capabilities of neural network structures. Specifically, we first decompose the event extraction problem into diverse intermediate subtasks, and each subtask is represented as a node in different layers of the network, the output of the nodes in the preceding layer is fed into the subsequent layer. Secondly, we propose pruning strategies to adapt the reasoning overhead to different problems. Finally, we have conducted extensive experiments on two widely used event extraction benchmarks to evaluate NetPrompt. The results demonstrated that NetPrompt significantly improved the event extraction performance compared to previous methods. ©2015 IEEE.

关键词： Neural network models

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Homophily Induced Contrastive Attributed Graph Clustering

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Chen, Man-Sheng Lai, Pei-Yuan Liao, De-Zhang Wang, Chang-Dong Lai, Jian-Huang Sun Yat-sen University School of Computer Science and Engineering Guangzhou China Guangxi Zhuang Autonomous Region Information Center Guangxi Key Laboratory of Digital Infrastructure Nanning China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Key Laboratory of Machine Intelligence and Advanced Computing Ministry of Education China South China Technology Commercialization Center Guangzhou China Guangdong Key Laboratory of Information Security Technology Guangzhou China

Attributed graph clustering, aiming to discover the underlying graph structure and partition the graph nodes into several disjoint categories, is a basic task in graph data analysis. Although recent efforts over graph contrastive clustering have achieved decent performances, most of them get accustomed to construct the positive neighbor set by the generated pseudo clustering information, directly ignoring the ready-made neighbor nodes and the underlying semantics of edges in a graph. How to well deal with the graph neighbor-specific information to facilitate the performance of graph contrastive clustering is still a challenging problem. Therefore, in this work, we propose a novel Homophily Induced Contrastive Attributed Graph Clustering (HomoCAGC) method, where the power of homophily is exploited in facilitating the performance of contrastive attributed graph clustering, while the pseudo homophily in a graph is also explored and distinguished. Especially, the node feature as reliable information guidance is used to compute the underlying feature-oriented pair-wise node similarity, based on which the positive node pairs in contrastive regularizer are adjusted for better node representation characterization. According to the refined node representations, a triplet self-supervised clustering objective is well-designed to ensure the output embedding is cluster-oriented, and suitable for the downstream clustering task. Extensive experiments on seven benchmarks are conducted to demonstrate the effectiveness of HomoCAGC. © 1991-2012 IEEE.

关键词： Attributed graph clustering contrastive learning graph homophily self-supervised clustering

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Carbon diffusion mechanism as an effective stability enhancement strategy:The case study of Ni-based catalyst for photothermal catalytic dry reforming of methane

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Chinese Journal of Catalysis 2025年第3期70卷 399-409页

作者： Dezheng Li Huimin Liu Xuewen Xiao Manqi Zhao Dehua He Yiming Lei School of Chemical and Environmental Engineering Liaoning University of TechnologyJinzhou 121000LiaoningChina Institute of Molecular Sciences of Orsay(ISMO) University Paris-Saclay and National Centre for Scientific Research(CNRS)Orsay91400France Key Laboratory of Organic Optoelectronics and Molecular Engineering of Ministry of Education Department of ChemistryTsinghua UniversityBeijing 100084China Department of Chemistry(Inorganic Chemistry) Faculty of SciencesAutonomous University of Barcelona(UAB)Cerdanyola del VallesBarcelona 08193Spain

Photothermal catalytic methane dry reforming(DRM)technology can convert greenhouse gases(***_(4)and CO_(2))into syngas(i.e.H_(2)and CO),providing more opportunities for reducing the greenhouse effect and achieving carbon *** the DRM field,Ni-based catalysts attract wide attention due to their low cost and high ***,the carbon deposition over Ni-based catalysts always leads to rapid deactivation,which is still a main *** improve the long-term stability of Ni-based catalysts,this work proposes a carbon-atom-diffusion strategy under photothermal conditions and investigates its effect on a Zn-doped Ni-based photothermal catalyst(Ni_(3)Zn@CeO_(2)).The photothermal catalytic behavior of Ni_(3)Zn@CeO_(2)can maintain more than 70 h in DRM *** the photocatalytic DRM activity of Ni_(3)Zn@CeO_(2)is 1.2 times higher than thermal catalytic *** functional theory(DFT)calculation and experimental characterizations indicate that Ni_(3)Zn promotes the diffusion of carbon atoms into the Ni_(3)Zn to form the Ni_(3)ZnC0.7 phase with body-centered cubic(bcc)structure,thus inhibiting carbon ***,in-situ diffuse reflectance infrared Fourier transform(DRIFT)spectroscopy and DFT calculation prove Ni_(3)Zn@CeO_(2)benefits the CH_(4)activation and inhibits the carbon deposition during the DRM *** inducing carbon atoms diffusion within the Ni_(3)Zn lattice,this work provides a straightforward and feasible strategy for achieving efficient photothermal catalytic DRM and even other CH_(4)conversion implementations with long-term stability.

关键词： Photothermal catalysis Methane dry reforming Ni-based catalyst Stability enhancement Carbon atom diffusion

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From Head to Tail: Efficient Black-box Model Inversion Attack via Long-tailed Learning

arXiv

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arXiv 2025年

作者： Li, Ziang Zhang, Hongguang Wang, Juan Chen, Meihui Hu, Hongxin Yi, Wenzhe Xu, Xiaoyang Yang, Mengda Ma, Chenjun Key Laboratory of Aerospace Information Security and Trusted Computing Ministry of Education School of Cyber Science and Engineering Wuhan University China Shanghai Innovation Institute China Ant Group China Department of Computer Science and Engineering University at Buffalo SUNY United States

Model Inversion Attacks (MIAs) aim to reconstruct private training data from models, leading to privacy leakage, particularly in facial recognition systems. Although many studies have enhanced the effectiveness of white-box MIAs, less attention has been paid to improving efficiency and utility under limited attacker capabilities. Existing black-box MIAs necessitate an impractical number of queries, incurring significant overhead. Therefore, we analyze the limitations of existing MIAs and introduce Surrogate Model-based Inversion with Long-tailed Enhancement (SMILE), a high-resolution oriented and query-efficient MIA for the black-box setting. We begin by analyzing the initialization of MIAs from a data distribution perspective and propose a long-tailed surrogate training method to obtain high-quality initial points. We then enhance the attack’s effectiveness by employing the gradient-free black-box optimization algorithm selected by NGOpt. Our experiments show that SMILE outperforms existing state-of-the-art black-box MIAs while requiring only about 5% of the query overhead. Our code is available at https://***/L1ziang/SMILE. Copyright © 2025, The Authors. All rights reserved.

关键词： Face recognition

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CPST-GAN: Conditional Probabilistic State Transition Generative Adversarial Network With the Biomedical Large Foundation Models

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IEEE Transactions on Neural Networks and Learning Systems 2025年 PP卷 PP页

作者： Wang, Qiong Xu, Luyun Shan, Yinglu Shen, Wenzhuo Li, Lou Bi, Xia-An Liu, Zhonghua Hunan Normal University Key Laboratory of Computing and Stochastic Mathematics Ministry of Education School of Mathematics and Statistics College of Information Science and Engineering Changsha410081 China Hunan Normal University College of Business Changsha410081 China Hunan Normal University College of Information Science and Engineering Changsha410081 China

The risk prediction of Alzheimer's disease (AD) is crucial for its early prevention and treatment. However, current risk prediction methods face challenges in effectively extracting and fusing multiomics features, particularly overlooking the multilevel evolutionary mechanisms of AD. This article combines biomedical large foundation models with the conditional generative adversarial network (GAN) to mine the evolutionary patterns of AD by considering the regulatory effect of genes on brain lesions. Specifically, we first use biomedical large foundation models to effectively construct high-quality imaging genetic features. Next, a conditional probabilistic state transition mathematical model is constructed to describe AD progression as state transitions of brain regions under genetic regulations. Based on the mathematical model, a conditional probabilistic state transition GAN (CPST-GAN) is proposed. This algorithm can mine the dynamic evolutionary patterns of AD by fusing brain imaging and genetic features to achieve risk prediction of AD. Finally, experiments on the public imaging genetics datasets validate the effectiveness and superiority of CPST-GAN in evolutionary pattern mining and risk prediction of AD. This article not only provides a reliable intelligence algorithm for early intervention of AD but also offers new insights for future research on AD pathogenesis. The code has been published at ***/fmri123456/CPST-GAN. © 2025 IEEE.

关键词： Generative adversarial networks

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Joint DNN Partition and Thread Allocation Optimization for Energy-Harvesting MEC Systems

Joint DNN Partition and Thread Allocation Optimization for E...

引用

Design, Automation and Test in Europe Conference and Exhibition

作者： Yizhou Shi Liying Li Yue Zeng Peijin Cong Junlong Zhou School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China National Key Laboratory for Novel Software Technology Nanjing University Nanjing China National Mobile Communications Research Laboratory Southeast University Nanjing China Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education Shanghai China

ISBN: (数字)9783982674100

ISBN: (纸本)9798331534646

Deep neural networks (DNNs) have demonstrated exceptional performance, leading to diverse applications across various mobile devices (MDs). Considering factors like portability and environmental sustainability, an increasing number of MDs are adopting energy harvesting (EH) techniques for power supply. However, the computational intensity of DNNs presents significant challenges for their deployment on these resource-constrained devices. Existing approaches often employ DNN partition or offloading to mitigate the time and energy consumption associated with running DNNs on MDs. Nonetheless, existing methods frequently fall short in accurately modeling the execution time of DNNs, and do not consider to use thread allocation for further latency and energy consumption optimization. To solve these problems, we propose a dynamic DNN partition and thread allocation method to optimize the latency and energy consumption of running DNNs on EH-enabled MDs. Specifically, we first investigate the relationship between DNN inference latency and allocated threads and establish an accurate DNN latency prediction model. Based on the prediction model, a DRL-based DNN partition (DDP) algorithm is designed to find the optimal partitions for DNNs. A thread allocation (TA) algorithm is proposed to reduce the inference latency. Experimental results from our test-bed platform demonstrate that compared to four benchmarking methods, our scheme can reduce DNN inference latency and energy consumption by up to 37.3% and 38.5%.

关键词： Energy consumption Instruction sets Heuristic algorithms Artificial neural networks Predictive models Prediction algorithms Inference algorithms Partitioning algorithms Resource management Optimization

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Progressive Human Motion Generation Based on Text and Few Motion Frames

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Zeng, Ling-An Wu, Gaojie Wu, Ancong Hu, Jian-Fang Zheng, Wei-Shi Sun Yat-sen University School of Artificial Intelligence Guangdong Zhuhai519082 China Sun Yat-sen University School of Computer Science and Engineering Guangdong Guangzhou510275 China Sun Yat-sen University School of Computer Science and Engineering China Guangdong Key Laboratory of Information Security Technology China Ministry of Education Key Laboratory of Machine Intelligence and Advanced Computing China Peng Cheng Laboratory China

Although existing text-to-motion (T2M) methods can produce realistic human motion from text description, it is still difficult to align the generated motion with the desired postures since using text alone is insufficient for precisely describing diverse postures. To achieve more controllable generation, an intuitive way is to allow the user to input a few motion frames describing precise desired postures. Thus, we explore a new Text-Frame-to-Motion (TF2M) generation task that aims to generate motions from text and very few given frames. Intuitively, the closer a frame is to a given frame, the lower the uncertainty of this frame is when conditioned on this given frame. Hence, we propose a novel Progressive Motion Generation (PMG) method to progressively generate a motion from the frames with low uncertainty to those with high uncertainty in multiple stages. During each stage, new frames are generated by a Text-Frame Guided Generator conditioned on frame-aware semantics of the text, given frames, and frames generated in previous stages. Additionally, to alleviate the train-test gap caused by multi-stage accumulation of incorrectly generated frames during testing, we propose a Pseudo-frame Replacement Strategy for training. Experimental results show that our PMG outperforms existing T2M generation methods by a large margin with even one given frame, validating the effectiveness of our PMG. Code is available here. © 1991-2012 IEEE.

关键词： Controllable Motion Generation Human Motion Text2Motion Generation

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Multi-routine-data driven spatio-temporal short-term predictions for surface ozone in China

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Air Quality, Atmosphere and Health 2025年 1-10页

作者： Zheng, Canju Shen, Hengqing Sun, Jianan Liu, Guangliang Cao, Haowei Zhang, Jie Gong, Xiang Xu, Da Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center Qilu University of Technology (Shandong Academy of Sciences) Jinan China Environment Research Institute Shandong University Shandong Qingdao 266237 China Ningbo Yonghuanyuan Environmental Engineering and Technology Co. LTD Zhejiang Ningbo 315012 China School of Mathematics and Physics Qingdao University of Science and Technology Qingdao 266061 China Shandong Provincial Key Laboratory of Computer Networks Shandong Fundamental Research Center for Computer Science Jinan China Qingdao Innovation Center of Artificial Intelligence Ocean Technology Qingdao 266061 China

Ozone (O3) is a major atmospheric pollutant, and accurate prediction of its concentrations remains challenging due to its complex nonlinear relationships with precursor compounds. Existing machine learning methods hav... 详细信息

Ozone (O₃) is a major atmospheric pollutant, and accurate prediction of its concentrations remains challenging due to its complex nonlinear relationships with precursor compounds. Existing machine learning methods have mainly focused on single-site or spatial predictions, lacking research on spatio-temporal short-term predictions based on simple factors. To address this gap, the MRD-O₃former, a deep learning model driven by multi-routine data, was developed to predict short-term hourly spatio–temporal surface ozone concentrations over China. The model exhibits strong spatio–temporal consistency, achieving a high correlation coefficient (r2=0.85∼0.90) and low normalized mean biases (NMBs) between -15% and 15% at the national scale compared to reanalysis ozone data. Both ablation experiments and permutation importance analysis reveal that historical ozone levels play a primary role in next-day ozone prediction, while meteorological factors such as wind speed and temperature also make significant contributions. Regional validation confirms the model’s effectiveness in the Beijing-Tianjin-Hebei region. Moreover, the study investigates the differential impact of crucial factors in urban and rural areas, revealing that historical ozone levels and meteorological factors significantly influence rural areas. However, the influence of historical ozone levels on urban ozone prediction is relatively small, especially during the summer. This suggests that urban ozone undergoes rapid formation and removal processes. These findings highlight the promising potential of deep learning techniques in accurately predicting spatiotemporal short-term ozone concentrations and interpreting the mechanism and source for ozone pollution. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Data-driven Multi-factor Ozone concentration Spatio-temporal prediction

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Dipole Antenna Arrays with Inherently High Port Isolation Levels

引用

IEEE Antennas and Wireless Propagation Letters 2025年

作者： Chen, Zhiyuan Lu, Dacheng Tang, Ming-Chun Hu, Kun-Zhi Li, Dajiang Liu, Heng Yi, Da Chongqing University of Posts and Telecommunications Key Laboratory of Industrial Internet of Things and Networked Control College of Automation Chongqing400065 China Chongqing University of Posts and Telecommunications Institute of Industrial Internet Chongqing China Chongqing University Key Laboratory of Dependable Service Computing in Cyber Physical Society Ministry of Education School of Microelectronics and Communication Engineering Chongqing400044 China

In this letter, the dipole antenna arrays with self-decoupling characteristics are developed. In comparison with the traditional electric dipole antennas, the developed dipole antenna can be considered as comprising an electric dipole and two magnetic dipoles while maintaining a relatively simple structural design. The multi-coupling cancellation between adjacent antenna elements was achieved to achieve high port isolation levels. To validate the effectiveness of the developed self-decoupling method, a 1×2 antenna array is simulated, fabricated, and measured. The results demonstrated port isolation level exceeds 25 dB over the entire operational bandwidth of 3.3-3.8 GHz, and a maximum isolation level of ⁓63 dB was achieved. Furthermore, a 1×6 antenna array is simulated to validate the extensibility of the developed self-decoupling method, where the isolation levels between all ports exceeds 25 dB across the entire operational bandwidth of 3.3-3.8 GHz. © 2002-2011 IEEE.

关键词： Structural dynamics

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